no code implementations • 17 May 2019 • Bryan Seybold, Emily Fertig, Alex Alemi, Ian Fischer
Variational autoencoders learn unsupervised data representations, but these models frequently converge to minima that fail to preserve meaningful semantic information.
no code implementations • ICLR 2018 • Alex Alemi, Ben Poole, Ian Fischer, Josh Dillon, Rif A. Saurus, Kevin Murphy
We present an information-theoretic framework for understanding trade-offs in unsupervised learning of deep latent-variables models using variational inference.
9 code implementations • 28 Nov 2017 • Joshua V. Dillon, Ian Langmore, Dustin Tran, Eugene Brevdo, Srinivas Vasudevan, Dave Moore, Brian Patton, Alex Alemi, Matt Hoffman, Rif A. Saurous
The TensorFlow Distributions library implements a vision of probability theory adapted to the modern deep-learning paradigm of end-to-end differentiable computation.
2 code implementations • NeurIPS 2018 • Sami Abu-El-Haija, Bryan Perozzi, Rami Al-Rfou, Alex Alemi
Graph embedding methods represent nodes in a continuous vector space, preserving information from the graph (e. g. by sampling random walks).
Ranked #62 on
Node Classification
on Citeseer
no code implementations • 5 May 2017 • Katerina Fragkiadaki, Jonathan Huang, Alex Alemi, Sudheendra Vijayanarasimhan, Susanna Ricco, Rahul Sukthankar
In this work, we present stochastic neural network architectures that handle such multimodality through stochasticity: future trajectories of objects, body joints or frames are represented as deep, non-linear transformations of random (as opposed to deterministic) variables.
53 code implementations • 23 Feb 2016 • Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, Alex Alemi
Recently, the introduction of residual connections in conjunction with a more traditional architecture has yielded state-of-the-art performance in the 2015 ILSVRC challenge; its performance was similar to the latest generation Inception-v3 network.
Ranked #376 on
Image Classification
on ImageNet